Difference between revisions of "Input.interp"
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# Merge the data.frame B with Iter. | # Merge the data.frame B with Iter. | ||
# Join data.frames A and B with rbind(). Columns: ''Iter,Index,Result''. | # Join data.frames A and B with rbind(). Columns: ''Iter,Index,Result''. | ||
| + | |||
| + | <rcode> | ||
| + | lmean <- function(parmean, parsd) {return(log(parmean)-log(1+(parsd^2)/(parmean^2))/2)} | ||
| + | lsd <- function(parmean, parsd) {return(log(1+(parsd^2)/(parmean^2)))} | ||
| + | |||
| + | input.interp <- function(res.char, n = 1000) { | ||
| + | res.char <- gsub(" ", "", res.char) | ||
| + | res.char <- gsub(",", ".", res.char) | ||
| + | plusminus <- gregexpr(paste("\\+-|", rawToChar(as.raw(177)), sep = ""), res.char) # saattaa osoittautua ongelmaksi enkoodauksen vuoksi | ||
| + | plusminus.length <- sapply(plusminus, length) | ||
| + | plusminus.exists <- unlist(plusminus)[cumsum(c(0, plusminus.length[-length(plusminus.length)])) + 1] > 0 | ||
| + | minus <- gregexpr("-", res.char) | ||
| + | minus.length <- sapply(minus, length) | ||
| + | minus.exists <- unlist(minus)[cumsum(c(0, minus.length[-length(minus.length)])) + 1] > 0 | ||
| + | brackets <- gregexpr("\\(.*\\)", res.char) # matches for brackets "(...)" | ||
| + | brackets.length <- as.numeric(unlist(lapply(brackets, attributes))) | ||
| + | brackets.pos <- unlist(brackets) | ||
| + | out <- list() | ||
| + | for(i in 1:length(res.char)) { | ||
| + | if(brackets.pos[i] >= 0) { | ||
| + | minus.relevant <- unlist(minus)[(cumsum(c(0, minus.length)) + 1)[i]:cumsum(minus.length)[i]] # Meni hieman monimutkaiseksi ylla olevan vektorisoinnin vuoksi | ||
| + | n.minus.inside.brackets <- sum(minus.relevant > brackets.pos[i] & minus.relevant < brackets.pos[i] + brackets.length[i]) | ||
| + | imean <- as.numeric(substr(res.char[i], 1, brackets.pos[i] - 1)) | ||
| + | if(n.minus.inside.brackets == 1) { | ||
| + | ici <- c(as.numeric(substr(res.char[i], brackets.pos[i] + 1, minus.relevant[minus.relevant > brackets.pos[i]] - 1)), as.numeric(substr(res.char[i], | ||
| + | minus.relevant[minus.relevant > brackets.pos[i]] + 1, brackets.pos[i] + brackets.length[i] - 2))) | ||
| + | isd <- sum(abs(ici - imean) / 2) / qnorm(0.975) | ||
| + | if((ici[2] - imean) / (ici[1] - imean) < 1.5) { | ||
| + | out[[i]] <- rnorm(n, imean, isd) | ||
| + | } else { | ||
| + | out[[i]] <- rlnorm(n, lmean(imean, isd), lsd(imean, isd)) # menee vaarin koska isd on laskettu normaalijakaumalle | ||
| + | } | ||
| + | } else | ||
| + | if(n.minus.inside.brackets == 2|n.minus.inside.brackets == 3) { | ||
| + | # consecutive.minuses <- minus.relevant + 1 == c(minus.relevant[2:length(minus.relevant)], 0) # turha jos oletetaan etta ensimmainen luku sulkujen sisalla on aina pienempi | ||
| + | ici <- c(as.numeric(substr(res.char[i], brackets.pos[i] + 1, minus.relevant[minus.relevant > brackets.pos[i]][2] - 1)), as.numeric(substr(res.char[i], | ||
| + | minus.relevant[minus.relevant > brackets.pos[i]][2] + 1, brackets.pos[i] + brackets.length[i] - 2))) | ||
| + | isd <- sum(abs(ici - imean) / 2) / qnorm(0.975) | ||
| + | out[[i]] <- rnorm(n, imean, isd) | ||
| + | } else out[[i]] <- paste("Unable to interpret \"", res.char[i], "\"", sep = "") | ||
| + | } else { | ||
| + | if(minus.exists[i]) { | ||
| + | minus.relevant <- unlist(minus)[(cumsum(c(0, minus.length)) + 1)[i]:cumsum(minus.length)[i]] | ||
| + | if(length(minus.relevant)==1) {if(as.numeric(substr(res.char[i], 1, minus.relevant - 1)) / as.numeric(substr(res.char[i], minus.relevant + 1, nchar(res.char[i]))) >= 1/100) { | ||
| + | out[[i]] <- runif(n, as.numeric(substr(res.char[i], 1, minus.relevant - 1)), as.numeric(substr(res.char[i], minus.relevant + 1, nchar(res.char[i]))))} else { | ||
| + | out[[i]] <- exp(runif(n, log(as.numeric(substr(res.char[i], 1, minus.relevant - 1))), log(as.numeric(substr(res.char[i], minus.relevant + 1, nchar(res.char[i]))))))} | ||
| + | } else {out[[i]] <- runif(n, as.numeric(substr(res.char[i], 1, minus.relevant[2] - 1)), as.numeric(substr(res.char[i], minus.relevant[2] + 1, nchar(res.char[i]))))} | ||
| + | } else { | ||
| + | if(plusminus.exists[i]) { | ||
| + | out[[i]] <- rnorm(n, as.numeric(substr(res.char[i], 1, plusminus[[i]][1] - 1)), as.numeric(substr(res.char[i], plusminus[[i]][1] + 1, nchar(res.char[i])))) | ||
| + | } | ||
| + | } | ||
| + | } | ||
| + | } | ||
| + | out | ||
| + | } | ||
| + | </rcode> | ||
| + | |||
| + | {{comment|# |Koodi on vielä vaiheessa, ottaa character vectorin alkiot ja antaa tulkinnat listana. Virhetoleranssi hyvin huono.|--[[User:Teemu R|Teemu R]] 03:09, 24 January 2012 (EET)}} | ||
Revision as of 01:09, 24 January 2012
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input.interp is an R function that interprets model inputs from a user-friendly format into explicit and exact mathematical format. The purpose is to make it easy for a user to give input without a need to worry about technical modelling details.
Question
What should be a list of important user input formats, and how should they be interpreted?
Answer
The basic feature is that if a text string can be converted to a meaningful numeric object, it will be. This function can be used when data is downloaded from Opasnet Base: if Result.Text contains this kind of numeric information, it is converted to numbers and fused with Result.
n is the number of iterations in the model. # is any numeric character in the text string.
| Example | Regular expression | Interpretation | Output in R |
|---|---|---|---|
| 12 000 | # # | 12000. Text is interpreted as number if space removal makes it a number. | as.numeric(gsub(" ", "", Result.text)) |
| 12,345 | #,# | 12.345. Commas are interpreted as decimal points. | as.numeric(gsub(",", ".", Result.text)) # Note! Do not use comma as a thousand separator! |
| -14,23 | -# | -14.23. Minus in the beginning of entry is interpreted as minus, not a sign for a range. | |
| 50 - 125 | # - # | Uniform distribution between 50 and 125 | data.frame(iter=1:n, result=runif(n,50,125)) |
| -12 345 - -23,56 | Uniform distribution between -12345 and -23.56. | ||
| 1 - 50 | # - # | Loguniform distribution between 1 and 50 (Lognormality is assumed if the ratio of upper to lower is => 30) | |
| 3.1 ± 1.2 or 3.1 +- 1.2 | # ± # or # +- # | Normal distribution with mean 3.1 and SD 1.2 | data.frame(iter=1:n, result=rnorm(n,3.1,1.2)) |
| 2.4 (1.8 - 3.0) | # (# - #) | Normal distribution with mean 2.4 and 95 % confidence interval from 1.8 to 3.0 | data.frame(iter=1:n, result=rnorm(n,2.4,(3.0-1.8)/2/1.96)) |
| 2.4 (2.0 - 3.2) | # (# - #) | Lognormal distribution with mean 2.4 and 95 % confidence interval from 2.0 to 3.0. Lognormality is assumed if the difference from mean to upper limit is => 50 % greater than from mean to lower limit. | |
| 24 - 35 (odds 5:1) | # - # (odds #:#) | Odds is five to one that the truth is between 24 and 35. How to calculate this, I don't know yet, but there must be a prior. | ⇤# : I am not sure whether this is actually needed. Who expresses uncertainties in this way? --Jouni 14:00, 28 December 2011 (EET) |
| 2;4;7 | Each entry (2, 4, and 7 in this case) are equally likely to occur. Entries can also be text. | ||
| * (in index, or explanatory, columns) | The result applies to all locations of this index. | With merge() function, this column is not used as a criterion when these rows are merged. |
How to actually make this happen in R?
- Make a temporary result temp by removing all spaces from Result.Text. Columns: Indices,Result.Result.Text,temp (Indices contains all explanatory columns.)
- Replace all "," with "."
- Check if there are parentheses "()". If yes, assume that they contain 95 % CI.
- Check if there are ranges "#-#".
- Divide the rows of the data.frame into two new data.frames with the same list of columns (Indices,Result).
- If temp is a syntactically correct distribution, take the row to data.frame A and replace Result with temp.
- Otherwise, take the row to data.frame B and replace Result with Result.Text if that is not NA.
- Create a new data.frame with index Iter = 1:n.
- Make a random sample from each probability distribution in data.frame A using Iter.
- Merge the data.frame B with Iter.
- Join data.frames A and B with rbind(). Columns: Iter,Index,Result.
--# : Koodi on vielä vaiheessa, ottaa character vectorin alkiot ja antaa tulkinnat listana. Virhetoleranssi hyvin huono. --Teemu R 03:09, 24 January 2012 (EET)
